Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

Wibowo, Totok Wahyu; Santosa, Sigit Heru Murti Budi; Susilo, Bowo; Purwanto, Taufik Hery

doi:10.30892/gtg.34129-640

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…Te clustering algorithm based on the maximum density within moving range selection proposed in this section considers that there is an obvious defciency at the point selection of the K-means clustering algorithm, and the initial point selection has a great infuence on the subsequent K-means clustering efect [18].…”

Section: Media Hotspot Discovery Model Based On Improvedmentioning

confidence: 99%

A Deep Neural Network-Based Approach to Media Hotspot Discovery

Luo

2023

Advances in Multimedia

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In recent years, with the rapid development of social network media, it has become a valuable research direction to quickly analyze these texts and find out the current hotspots from them in real time. To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. Then, the K-means algorithm is optimized for the initial point selection problem, and a clustering algorithm based on the maximum density selection method in the moving range is proposed. Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. The accuracy of the proposed media hotspot discovery method and the comprehensive evaluation of the computing time have been verified experimentally.

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Section: Media Hotspot Discovery Model Based On Improvedmentioning

confidence: 99%

A Deep Neural Network-Based Approach to Media Hotspot Discovery

Luo

2023

Advances in Multimedia

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“…The social media data undergoes a filtering process to ascertain the presence of tourist-related content. Filtering is based on identifying keywords commonly associated with tourism in Twitter and Flickr posts and is supplemented by a location-based screening mechanism to eliminate spam and duplicate posts [14]. The Flickr data filtering results demonstrate a higher concentration of tourism-related posts around Jalan Malioboro and Yogyakarta Palace.…”

Section: Methodsmentioning

confidence: 99%

“…The research area is in Yogyakarta, a dynamic city on Java Island [13]. Yogyakarta is renowned for its rich cultural heritage, historical edifices, and landmarks and for its large student population [14]. Besides its well-established attractions, the city is famous for its lively student culture [15].…”

Section: Study Areamentioning

confidence: 99%

A review from a geospatial perspective on how COVID-19 is affecting tourist activity in Yogyakarta city

Wibowo,

Santosa,

Susilo

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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The COVID-19 pandemic has had a huge impact on Indonesia, as many other nations throughout the globe, particularly on the travel and tourism industry. The most noticeable effect is the decline in tourist visitation, which fell by over 75% in 2020 compared to the prior year. Businesses and workers in the tourism industry have been significantly impacted by the fall in visitor numbers, particularly in Yogyakarta, one of the most well-liked tourist sites in the nation. This study intends to investigate the geographical effects of the COVID-19 pandemic on tourism-related activities. A strategy to determine changes in travel behaviour before and during the COVID-19 pandemic uses social media data, such as Flickr and Twitter. Both social media has been extensively used in tourism related studies in the past. Because December is the busiest month for tourism, Twitter data from that month was chosen as the sample. The selected sample ranges are for 2019, 2020, and 2021. While Flickr data covers from 2018 to 2023, to generate a different perspective than that of Twitter data. The study's findings demonstrate how limitations on community activities significantly influence the traditionally popular tourist attractions. Public spaces, dining establishments, and even hotels are preferred travel destinations by tourists.

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“…Accessibility becomes an essential aspect in SE and tourism in which tourists would more easily visit TDs that have good accessibility which stimulates them to come to tourist places (Chin et al, 2014); the accessibility is the pedestrian path. The physical quality of the pedestrian path is essential in making it easier for tourists to explore tourist destinations (Wibowo et al, 2021). Accessibility will affect visitors feel comfortable and not comfortable, which will affect visitors' SE (Jaelani et al, 2020).…”

Section: Accessibilitymentioning

confidence: 99%

Impact of Gender on Self-Efficacy: Evidence From Indonesia’s Karo Tourism

Ginting¹,

RAHMAN²,

Nasution³

2022

GTG

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Self-efficacy in tourist destination can influence the tourists’ level of satisfaction which may depend on how much the destination has fulfilled their needs. This research aims at investigating the relationship between gender preference and self-efficacy in tourist destination in Karo regency. Female tourists are usually significantly different, in which male’s self-efficacy tend to be higher than female’s. This research used a qualitative and quantitative method to obtain the varieties of perception, such as accessibility, comfort, confidence, and safety. The result reveals self-efficacy isindeed different for local male and female visitors; however, the male and female tourists from abroad do not indicate significant difference. The level of self-efficacy (LSE) for females is not always lower than males and, even, the activities cannot limit the LSE for males and females. The tourists’ activities in the masculine category proved that the females’ SE is higher than males. However, the males’ SE proved to be higher than females are only found among local people; however, the tourists’s SE between males and females look the same. In the development of tourism which is based on the tourists’ SE, preference of gender does not need to be considered; however, if it is done on the basis locals’ SE, then the preference of gender is really needed.

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Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

Cited by 9 publications

References 29 publications

A Deep Neural Network-Based Approach to Media Hotspot Discovery

A Deep Neural Network-Based Approach to Media Hotspot Discovery

A review from a geospatial perspective on how COVID-19 is affecting tourist activity in Yogyakarta city

Impact of Gender on Self-Efficacy: Evidence From Indonesia’s Karo Tourism

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